An Access Pattern-aware Hybrid Learning-based and Conventional Mapping for Solid-State Drives

Learning-based address mapping strategy has been proposed as an alternative to the conventional one-to-one mapping strategy for logical to physical address translation in flash-based Solid-State Drives (SSDs). This strategy employs a machine learning (ML)-based index to replace the memory-intensive mappings, thereby saving memory space. However, current learning-based mapping strategies mainly focus on the sequential characteristics of I/O workloads, making them less effective in scenarios with frequent updates. In this work, we introduce an access pattern-aware hybrid learning-based and conventional mapping scheme for SSDs, called APH-FTL. This approach features a machine learning-driven data classifier that employs distinct address mapping strategies for data requests with varying access patterns. Additionally, we develop a benefit-guided space regulation strategy to manage in-device memory space effectively and provide a solution to the challenge of mapping policies transformation. We evaluate APH-FTL through simulations using various real-world workloads. Experimental results demonstrate that APH-FTL significantly improves performance, reducing response time by up to 76.59% and write amplification (WA) by up to 82.97% compared with state-of-the-art schemes.